Pattern recognition techniques, such as artificial neural networks are finding increased application in solving a variety of problems such as optical character recognition, voice recognition, and military target identification. In the automotive industry in particular, pattern recognition techniques have now been applied to identify various objects within the passenger compartment of the vehicle, such as a rear facing child seat, as well as to identify threatening objects with respect to the vehicle, such as an approaching vehicle about to impact the side of the vehicle. In this regard, reference is made, for example, to copending U.S. patent application Ser. No. 08/239,978 filed May 9, 1994, now abandoned, Ser. No. 08/247,760 filed May 23, 1994, now abandoned and Ser. No. 08/798,029 filed Feb. 6, 1997, now abandoned which are entirely incorporated herein by reference. Pattern recognition techniques have also been applied to sense automobile crashes for the purpose of determining whether or not to deploy an airbag or other passive restraint, or to tighten the seatbelts, cutoff the fuel system, or unlock the doors after the crash. In this regard, reference is made, for example, to copending U.S. patent application Ser. No. 08/476,076 filed Jun. 7, 1995, now U.S. Pat. No. 5,684,701 which is entirely incorporated herein by reference. Heretofore, pattern recognition techniques have not been applied to forecast the severity of automobile crashes for the purpose of controlling the flow of gas into or out of an airbag to tailor the airbag inflation characteristics or to control seatbelt retractors, pretensioners or energy dissipators to the crash severity. Furthermore, such techniques have also not been used for the purpose of controlling the flow of gas into or out of an airbag to tailor the airbag inflation characteristics to the size, position or relative velocity of the occupant or other factors such as seatbelt usage, seat and seat back positions, headrest position, vehicle velocity, etc.
xe2x80x9cPattern recognitionxe2x80x9d as used herein means any system which processes a signal that is generated by an object, or is modified by interacting with an object, in order to determine which one of a set of classes the object belongs to. In this case, the object can be a vehicle with an accelerometer which generates a signal based on the deceleration of the vehicle. Such a system might determine only that the object is or is not a member of one specified class. (e.g., airbag required crashes), or it might attempt to assign the object to one of a larger set of specified classes, or find that it is not a member of any of the classes in the set. One such class might consist of vehicles undergoing a crash of a certain severity into a pole. The signals processed are generally electrical signals coming from transducers which are sensitive to either acceleration, or acoustic or electromagnetic radiation and, if electromagnetic, they can be either visible light, infrared, ultraviolet or radar.
To xe2x80x9cidentifyxe2x80x9d as used herein means to determine that the object belongs to a particular set or class. The class may be one containing all frontal impact airbag-desired crashes into a pole at 20 mph, one containing all events where the airbag is not required, or one containing all events requiring a triggering of both stages of a dual stage gas generator with a 15 millisecond delay between the triggering of the first and second stages.
All electronic crash sensors currently used in sensing frontal impacts include accelerometers which detect and measure the vehicle accelerations during the crash. The accelerometer produces an analog signal proportional to the acceleration experienced by the accelerometer and hence the vehicle on which it is mounted. An analog to digital converter (ADC) transforms this analog signal into a digital time series. Crash sensor designers study this digital acceleration data and derive therefrom computer algorithms which determine whether the acceleration data from a particular crash event warrants deployment of the airbag. This is usually a trial and error process wherein the engineer or crash sensor designer observes data from crashes where the airbag is desired and when it is not needed, and other events where the airbag is not needed. Finally, the engineer or crash sensor designer settles on the xe2x80x9crulesxe2x80x9d for controlling deployment of the airbag which are programmed into an algorithm which seem to satisfy the requirements of the crash library, i.e., the crash data accumulated from numerous crashes and other events. The resulting algorithm is not universal and most such engineers or crash sensor designers will answer in the negative when asked whether their algorithm will work for all vehicles. Such an algorithm also merely determines that the airbag should or should not be triggered. Heretofore, no attempt has been made to ascertain or forecast the eventual severity of the crash or, more specifically, the velocity change versus time of the passenger compartment during the crash from the acceleration data obtained from the accelerometer.
Several papers have been published pointing out some of the problems and limitations of electronic crash sensors which are mounted out of the.crush zone of the vehicle, usually in a protected location in the passenger compartment of the vehicle. The crush zone is defined, for the purposes herein, as that portion of the vehicle which has crushed at the time that the crash sensor must trigger deployment of the restraint system. These sensors are frequently called single point crash sensors. Technical papers which discuss the limitations of current single point sensors along with discussions of the theory of crash sensing, which are relevant to this invention and which are included entirely herein by reference, are:
1) Breed, D. S. and Castelli, V. xe2x80x9cProblems in Design and Engineering of Air Bag Systemsxe2x80x9d, Society of Automotive Engineers Paper SAE 880724, 1988
2) Breed, D. S., Castelli, V. xe2x80x9cTrends in Sensing Frontal Impactxe2x80x9d, Society of Automotive Engineers Paper SAE 890750, 1989.
3) Breed, D. S., Sanders, W. T. and Castelli, V. xe2x80x9cA Critique of Single Point Crash Sensingxe2x80x9d, Society of Automotive Engineers Paper SAE 920124, 1992.
4) Breed, D. S., Sanders, W. T. and Castelli, V. xe2x80x9cA Complete Frontal Crash Sensor Systemxe2x80x94Ixe2x80x9d, Society of Automotive Engineers Paper SAE 930650, 1993.
5) Breed, D. S. and Sanders, W. T. xe2x80x9cUsing Vehicle Deformation to Sense Crashesxe2x80x9d, Presented at the International Body and Engineering Conference, Detroit Mich., 1993.
6) Breed, D. S., Sanders, W. T. and Castelli, V., xe2x80x9cA Complete Frontal Crash Sensor Systemxe2x80x94IIxe2x80x9d, Proceedings Enhanced Safety of Vehicles Conference, Munich, 1994, Published by the US Department of Transportation, National Highway Traffic Safety Administration, Washington, D.C.
These papers demonstrate, among other things, that there is no known theory which allows an engineer to develop an algorithm for sensing crashes and selectively deploying the airbag except when the sensor is located in the crush zone of the vehicle. These papers show that, in general, there is insufficient information within the acceleration signal measured in the passenger compartment to sense all crashes. Another conclusion suggested by these technical papers is that if an algorithm can be found which works for one vehicle, it will also work for all vehicles since it is possible to create any crash pulse measured in one vehicle, in any vehicle. Note in particular SAE paper 920124 referenced above.
In spite of, the problems associated with finding the optimum crash sensor algorithm, many vehicles on the road today have electronic single point crash sensors. Some of the problems associated with single point sensors have the result that an out-of-position occupant who is sufficiently close to the airbag at the time of deployment will be injured or killed by the deployment itself. Fortunately, systems are now being developed which monitor the location of occupants within the vehicle and can suppress deployment of the airbag if the occupant is more likely to be injured by the deployment than by the accident. These systems do not, however, currently provide the information necessary for the control of airbag systems, or the combination of seatbelt and airbag systems, which have the capability of varying the flow of gas into or out of the airbag and thus to tailor the airbag to the size and weight of the occupant (or possibly another morphological characteristic of the occupant), as well as to the position, velocity and seatbelt use of the occupant. More particularly, no such system exists which uses pattern recognition techniques to match the airbag deployment or gas discharge from the airbag to the severity of the crash or the size, weight, position, velocity and seatbelt use of an occupant.
Since there is insufficient information in the acceleration data, as measured in the passenger compartment, to sense all crashes and since some of the failure modes of published single point sensor algorithms can be easily demonstrated using the techniques of crash and velocity scaling described in the above-referenccd technical papers, and moreover since the process by which engineers develop algorithms is based on trial and error, pattern recognition techniques such as neural network should be able to be used to create an algorithm based on training the system on a large number of crash and non-crash events which, although not perfect, will be superior to all others. This in fact has proved to be true and is the subject the invention disclosed in copending U.S. patent application Ser. No. 08/476,076, now U.S. Pat. No. 5,684,701 referenced above. That invention is based on the ability of neural networks to forecast, based on the first part of the crash pulse, that the crash will be of a severity which requires that an airbag be deployed. As will be discussed in greater detail below, an improvement on that invention, which is the subject of the instant invention, carries this process further by using a neural network pattern recognition system to forecast the velocity change of the crash over time so that the inflation and/or deflation of the airbag, and the seatbelt, can be optimized. This invention further contemplates the addition of the pattern recognition occupant position and velocity determination means disclosed in copending patent applications Ser. Nos. 08/239,978, 08/247,760 and 08/798,029 also referenced above. Finally, the addition of the weight of the occupant is contemplated to provide a measure of the occupants inertia or momentum as an input to the system. The combination of these systems in various forms can be called xe2x80x9csmart airbagsxe2x80x9d or xe2x80x9csmart restraintsxe2x80x9d which will be used as equivalents herein. In a preferred implementation, the crash severity is not explicitly forecasted but rather, the value of a control parameter used to control the flows of inflator gas in or out of the airbag is forecasted.
Smart airbags can take several forms which can be roughly categorized into four evolutionary stages, which will hereinafter be referred to as Phase 1 (2,3,4) Smart Airbags, as follows:
1) Occupant sensors such as the disclosed in the U.S. patent applications cross-referenced above use various technologies to turn off the airbag where there is a rear facing child seat present or if either the driver or passenger is out-of-position to where he/she is more likely to be injured by the airbag than from the accident.
2) Occupant sensors will be used along with variable inflation or deflation rate airbags to adjust the inflation/deflation rate to match the occupant first as to his/her position and then to his/her morphology. The occupant sensors disclosed in the cross-referenced patent applications will also handle this with the possible addition of an occupant weighing system. One particular weight measuring system which makes use of strain gages mounted onto the seat supporting structure is disclosed in copending U.S. patent application Ser. No. 08/474,784 filed Jun. 7, 1995, now U.S. Pat. No. 5,748,473 which is included entirely herein by reference. At the end of this phase, little more can be done with occupant measurement or characterization systems.
3) The next improvement, and the subject of the instant invention, is to use a pattern recognition system such as neural networks as the basis of a crash sensor not only to determine if the airbag should be deployed but also to predict the crash severity from the pattern of the initial portion of the crash pulse. Additionally, the crash pulse will continue to be monitored even after the decision has been made to deploy the airbag to see if the initial assumption of the crash type based on the pattern up to the deployment decision was correct. If the pattern changes indicating a different crash type, the flow rate to the airbag can be altered on the fly, i.e., substantially instantaneously.
4) Finally, anticipatory sensing using pattern recognition techniques such as neural networks will be used to identify the crash before it takes place and select the deployment characteristics of the airbag to match the anticipated crash with the occupant size and position. Such an anticipatory sensor is described in copending U.S. patent application Ser. No. 08/247,760 filed May 23, 1994.
Any of these phases can be combined with various methods of controlling the pretensioning, retraction or energy dissipation characteristics of the seatbelt. Although the main focus of this invention is the control of the flows of gas into and out of the airbag, it is to be recognized that control of the seatbelt can also benefit from this invention and that the condition of the seatbelt can be valuable input information into the pattern recognition system.
When a crash commences, the vehicle starts decelerating and an accelerometer located in the passenger compartment begins sensing this deceleration and produces an electronic signal which varies over time in proportion to the magnitude of the deceleration. This signal contains information as to the type of the crash which can be used to identify the crash. A crash into a pole gives a different signal than a crash into a rigid barrier, for example, even during the early portion of the crash before the airbag triggering decision has been made. A neural network pattern recognition system can be trained to recognize and identify the crash type from this early signal and further to forecast ahead the velocity change versus time of the crash. Once this forecast is made, the severity and timing of the crash can be predicted. Thus, for a rigid barrier impact, an estimate of the eventual velocity change of the crash can be made and the amount of gas needed in the airbag to cushion an occupant as well as the time available to get that amount of gas into the airbag can be determined and used to control the airbag inflation.
Taking another example, that of a crash into a highway energy absorbing crash cushion. In this case, the neural-network-based sensor determines that this is a very slow crash and causes the airbag to inflate.more slowly thereby reducing the incidence of collateral injuries such as broken arms and eye lacerations.
In both of these cases, the entire decision making process takes place before the airbag deployment is initiated. In another situation where a soft crash is preceded by a hard crash, such as might happen if a pole were in front of a barrier, the neutral network system would first identify the soft pole crash and begin slowly inflating the airbag. However, once the barrier impact began, the system would recognize that the crash type has changed and recalculate the amount and timing of the introduction of gas into the airbag and send appropriate commands to the inflation control system of the airbag to possibly vary the introduction of gas into the airbag.
There are many ways of controlling the inflation of the airbag and several are now under development by the inflator companies. One way is to divide the airbag into different charges and to initiate these charges independently as a function of time to control the airbag inflation. An alternative is to always generate the maximum amount of gas but to control the amount going into the airbag, dumping the rest into the atmosphere. A third way is to put all of the gas into the airbag but control the outflow of the gas from the airbag through a variable vent valve. For the purposes herein, all controllable apparatus for varying the gas flow into or out of the airbag over time will be considered as a gas control module whether the decision is made at the time of initial airbag deployment, at one or more discrete times later or continuously during the crash event.
The use of pattern recognition techniques in crash sensors has another significant advantage in that it can share the same pattern recognition hardware and software with other systems in the vehicle. Pattern recognition techniques have proven to be effective in solving other problems related to airbag passive restraints. In particular, the identification of a rear-facing child seat located on the front passenger seat, so that the deployment of the airbag can be suppressed, has been demonstrated. Also, the use of pattern recognition techniques for the classification of vehicles about to impact the side of the subject vehicle for use in anticipatory side impact crash sensing shows great promise. Both of these pattern recognition systems, as well as others under development, can use the same computer system as the crash sensor and prediction system of this invention. Moreover, both of these systems will need to interact with, and should be part of, the diagnostic module used for frontal impacts. It would be desirable for cost and reliability considerations, therefore, for all such systems to use the same computer system. This is particularly desirable since computers designed specially for solving pattern recognition problems, such as neural-computers, are now available and can be integrated into a custom application specific integrated circuit (ASIC).
The smart airbag problem is complex and difficult to solve by ordinary mathematical methods. Looking first at the influence of the crash pulse, the variation of crash pulses in the real world is vast and quite different from the typical crashes run by the automobile industry as reported in the above-referenced technical papers. It is one problem to predict that a crash is of a severity level to require the deployment of an airbag. It is quite a different problem to predict exactly what the velocity versus time function will be and then to adjust the airbag inflation/deflation control system to make sure that just the proper amount of gas is in the airbag at all times even without considering the influence of the occupant. To also simultaneously consider the influence of occupant size, weight, position and velocity renders this problem for all practical purposes unsolvable by conventional methods.
On the other hand, if a pattern recognition system such as a neural network is used and trained on a large variety of crash acceleration segments, as described in U.S. Pat. No. 5,684,701 referenced above, and a setting for the inflation/deflation control system is specified for each segment, then the problem can be solved. Furthermore, inputs from the occupant position and occupant weight sensors can also be included. The result will be a training set for the neural network involving many millions, and perhaps tens of millions, of data sets or vectors as every combination of occupancy characteristics and acceleration segment is considered. Fortunately, the occupancy data can be acquired independently and is currently being done for solving the out-of-position problem of Phase 1 smart airbags. The crash data is available in abundance and more can be created using the crash and velocity scaling techniques described in the above-referenced papers. The training using combinations of the two data sets, which must also take into account occupant motion which is not adequately represented in the occupancy data, can then be done by computer. Even the computer training process is significant to tax current PC capabilities and in some cases the use of a super-computer may be warranted.
The present invention uses pattern recognition techniques such as a neural network, or neural-network-derived algorithm, to analyze the digitized accelerometer data (also referred to as acceleration data herein) created during a crash and, in some cases, occupant size, position, seatbelt use, weight and velocity data, and, in other cases, data from an anticipatory crash sensor, to determine not only if and when a passive restraint such as an airbag should be deployed but also to control the flow of gas into or out of the airbag. Principal objects and advantages include:
1) To provide a single point sensor including an accelerometer which makes maximum use of the information in the acceleration data to determine not only whether an airbag should be deployed but the rate of deployment as required for Phase 3 Smart Airbags.
2) To provide a single point sensor including an accelerometer which makes maximum use of the information in the acceleration data to determine not only whether an airbag should be deployed but the total amount of gas which should be used to inflate the airbag as required for Phase 3 Smart Airbags.
3) To provide a single point sensor including an accelerometer which makes maximum use of the information in the acceleration data to determines the gas flow control parameter value for use by a gas control module to control the flow of gas into or out of an airbag as required for Phase 3 Smart Airbags.
4) To provide a single computer system which can perform several different pattern recognition functions within an automobile or other vehicle including, for example, crash sensing and severity prediction, anticipatory sensing, identification of an occupant located within the vehicle passenger compartment and determination of the position and velocity of the occupant.
5) To provide a crash sensor and crash severity prediction algorithm which is derived by training using a set of data derived from staged automobile crashes and non-crash events as well as other analytically derived data, as required for Phase 3 Smart Airbags.
6) To provide a crash sensor and crash severity prediction algorithm based on pattern recognition techniques.
7) To provide a crash sensor and crash severity prediction algorithm which uses other data in addition to acceleration data derived from the crash wherein this data is combined with acceleration data and, using pattern recognition techniques, the need for deployment and the rate of deployment of a passive restraint is determined.
8) To provide a crash sensor and crash severity prediction algorithm using data from an anticipatory sensor and an occupant position and velocity sensing system to optimize the deployment of a passive restraint system taking into account the crash severity and occupant dynamics to minimize injuries to the occupant as required for Phase 4 Smart Airbags.
9) To provide an electronic module which combines the functions of crash sensing and crash severity prediction, occupant position and velocity sensing, anticipatory sensing (as required for Phase 4 Smart Airbags) and airbag system diagnostics.
10) To provide a Phase 1, Phase 2, Phase 3 or Phase 4 Smart Airbag system which uses a neural computer.
Other objects and advantages of this invention will become apparent from the disclosure which follows.
Generally, the present invention provides a smart airbag system which optimizes the deployment of an occupant protection apparatus in a motor vehicle, such as an airbag, to protect an occupant of the vehicle in a crash. The system includes an accelerometer mounted to the vehicle for sensing accelerations of the vehicle and producing an analog signal representative thereof; an electronic converter for receiving the analog signal from the sensor and for converting the analog signal into a digital signal, and a processor which receives the digital signal. The processor includes a pattern recognition system and produces a deployment signal when the pattern recognition system determines that the digital signal contains a pattern characteristic of a vehicle crash requiring occupant protection and further produces a signal which controls the flow of inflator gas into or out of the airbag. In some implementations, the system also includes occupant position and velocity sensing means which outputs a signal which is also used by the processor in producing the signal which controls the flow of gas into or out of the airbag.
In one particular embodiment, the sensor system for controlling the deployment of the occupant protection apparatus comprises sensor means mounted on the vehicle for sensing accelerations of the vehicle, e.g., in a position to sense frontal, rear and/or side impacts into the vehicle, and producing an analog signal representative thereof, converting means for receiving the analog signal and converting it into a digital signal, and processing means for receiving and processing the digital signal. The processing means comprise pattern recognition means for determining if the digital signal contains a pattern characteristic of a vehicle crash requiring deployment of the occupant protection apparatus and if so, produce a deployment control signal. The sensor system also includes control means coupled to the processing means and responsive to the deployment control signal for controlling the rate of deployment of the occupant protection apparatus. The pattern recognition means comprises a neural network or a neural computer coupled to the converting means. The converting means may derive the digital signal from the integral of the analog signal. The processing means may also be arranged to detect when the occupant(s) to be protected by the deployable occupant protection apparatus is/are out-of-position and thereupon to suppress deployment of the occupant protection apparatus. In one embodiment, the deployable occupant protection apparatus is a passenger side airbag and the control means control the rate of a flow of inflation fluid into the passenger side airbag, the processing means also being optionally designed to detect the presence of a rear-facing child scat positioned on the passenger scat and thereupon to suppress deployment of the passenger side airbag. The sensor means may comprise an anticipatory sensor or possibly a sensor for a collision avoidance system or possibly an acceleration measurement system which measures accelerations in at least two directions.
In an enhanced embodiment, the system includes detecting means for detecting the position, size, velocity, and/or weight of the occupant to be protected by the deployable occupant protection apparatus. The detecting means are designed to affect the control means in order to adjust the deployment rate of the occupant protection apparatus depending on the detected position, size, velocity, and/or weight of the occupant.
If the processing means comprises a neural computer, additional data may be input thereto to be used by the pattern recognition means, e.g., data from an anticipatory sensor or data from a collision avoidance sensor. The neural computer can also diagnoses the apparatus readiness.
The method for obtaining an algorithm for use with a computer-based crash sensor to control the deployment rate of a deployable occupant protection device in a vehicle in a vehicle crash in accordance with the invention comprises the steps of:
(a) obtaining digital crash data representative of the vehicle for which the crash sensor is intended to be used, the crash data being obtained from deployment desired crashes, crashes in which deployment is not desired and other events, the combination of all such crashes and events constituting a crash library for the vehicle;
(b) designing a candidate pattern recognition algorithm;
(c) training the pattern recognition algorithm to produce an output to control the inflation or deflation rate of the deployable occupant protection system for the events of the crash library using a pattern recognition computer program and the crash library until the control output errors are reduced to a minimum, resulting in a trained neural network;
(d) testing the trained pattern recognition algorithm using additional crashes and events representative of the vehicle;
(e) optionally redesigning the pattern recognition algorithm when testing performance is unsatisfactory, and repeating training and testing steps(c) and (d); and
(f) outputting from the pattern recognition program the resulting crash sensor and inflation control algorithm.
The method for sensing a crash of a vehicle to determine the deployment rate of a deployable occupant protection device in the vehicle in accordance with the invention comprises the steps of:
(a) obtaining an acceleration signal from an accelerometer mounted on the vehicle;
(b) converting the acceleration signal into a digital time series;
(c) entering the digital time series data into a first series of input nodes of a neural network;
(d) performing a mathematical operation on the data from each of the first series of input nodes and inputting the operated-on data into a second series of nodes wherein the operation performed on the data from each of the first series of input node prior to inputting the operated-on data to the second series node is different from the operation performed on the data from the others of the first series of input nodes;
(e) combining the operated-on data from all of the input nodes into each second series node to form a value at each second series node;
(f) performing a mathematical operation on each of the values on the second series of nodes and inputting the operated-on data into an output series of nodes wherein the operation performed on each of the second series node data prior to inputting the operated-on value to an output series node is different from that operation performed on some other second series node data;
(g) combining the operated-on data from all of the second series nodes into each output series node to form a value at each output series node; and,
(h) outputting a value to an gas flow control module from the output node to control the rate of deployment of a deployable device.
Optionally, a third series of nodes is placed between the second series of nodes and the output series of nodes and the operated-on data from the second series of nodes is input into the third series of nodes and operated on values from the third series of nodes is input into the output nodes.